All Outputs (2)

Unsupervised temporospatial neural architecture for sensorimotor map learning. (2019)
Journal Article
EZENKWU, C.P. and STARKEY, A. 2021. Unsupervised temporospatial neural architecture for sensorimotor map learning. IEEE transactions on cognitive and developmental systems [online], 13(1), pages 223-230. Available from: https://doi.org/10.1109/TCDS.2019.2934643

The ability to learn the sensorimotor maps of unknown environments without supervision is a vital capability of any autonomous agent, be it biological or artificial. An accurate sensorimotor map should be able to encode the agent's world and equip it... Read More about Unsupervised temporospatial neural architecture for sensorimotor map learning..

Machine autonomy: definition, approaches, challenges and research gaps. (2019)
Conference Proceeding
EZENKWU, C.P. and STARKEY, A. 2019. Machine autonomy: definition, approaches, challenges and research gaps. In Arai, K., Bhatia, R. and Kapoor, S. (eds.) Intelligent computing: proceedings of the 2019 Computing conference, 16-17 July 2019, London, UK. Advances in intelligent systems and computing, 997. Cham: Springer [online], volume 1, pages 335-358. Available from: https://doi.org/10.1007/978-3-030-22871-2

The processes that constitute the designs and implementations of AI systems such as self-driving cars, factory robots and so on have been mostly hand-engineered in the sense that the designers aim at giving the robots adequate knowledge of its world.... Read More about Machine autonomy: definition, approaches, challenges and research gaps..